import gdown
import polars as pl
import pandas as pd
import numpy as np
import sqlite3
import plotly.express as px
import plotly.graph_objects as go

drive_loc = 'https://drive.google.com/uc?id=1V2GCHGt2dkFGqVBeoUFckU4IhUgk4ocQ'

download_LC = 'downloaded_file.csv'
gdown.download(drive_loc, download_LC, quiet=False)

Jobs = pl.read_csv(download_LC)

Downloading...
From (original): https://drive.google.com/uc?id=1V2GCHGt2dkFGqVBeoUFckU4IhUgk4ocQ
From (redirected): https://drive.google.com/uc?id=1V2GCHGt2dkFGqVBeoUFckU4IhUgk4ocQ&confirm=t&uuid=d2c62f77-a3a3-4c81-bb4c-ea5c60293206
To: C:\Users\jtsto\OneDrive\Documents\03 BU SPRING 2025\AD 688\Mod04_Lab01\downloaded_file.csv
100%|██████████| 717M/717M [01:06<00:00, 10.8MB/s]

Jobs.head()

Jobs.shape

(72498, 131)

columns = Jobs.columns

for col in columns:
    print(col)

ID
LAST_UPDATED_DATE
LAST_UPDATED_TIMESTAMP
DUPLICATES
POSTED
EXPIRED
DURATION
SOURCE_TYPES
SOURCES
URL
ACTIVE_URLS
ACTIVE_SOURCES_INFO
TITLE_RAW
BODY
MODELED_EXPIRED
MODELED_DURATION
COMPANY
COMPANY_NAME
COMPANY_RAW
COMPANY_IS_STAFFING
EDUCATION_LEVELS
EDUCATION_LEVELS_NAME
MIN_EDULEVELS
MIN_EDULEVELS_NAME
MAX_EDULEVELS
MAX_EDULEVELS_NAME
EMPLOYMENT_TYPE
EMPLOYMENT_TYPE_NAME
MIN_YEARS_EXPERIENCE
MAX_YEARS_EXPERIENCE
IS_INTERNSHIP
SALARY
REMOTE_TYPE
REMOTE_TYPE_NAME
ORIGINAL_PAY_PERIOD
SALARY_TO
SALARY_FROM
LOCATION
CITY
CITY_NAME
COUNTY
COUNTY_NAME
MSA
MSA_NAME
STATE
STATE_NAME
COUNTY_OUTGOING
COUNTY_NAME_OUTGOING
COUNTY_INCOMING
COUNTY_NAME_INCOMING
MSA_OUTGOING
MSA_NAME_OUTGOING
MSA_INCOMING
MSA_NAME_INCOMING
NAICS2
NAICS2_NAME
NAICS3
NAICS3_NAME
NAICS4
NAICS4_NAME
NAICS5
NAICS5_NAME
NAICS6
NAICS6_NAME
TITLE
TITLE_NAME
TITLE_CLEAN
SKILLS
SKILLS_NAME
SPECIALIZED_SKILLS
SPECIALIZED_SKILLS_NAME
CERTIFICATIONS
CERTIFICATIONS_NAME
COMMON_SKILLS
COMMON_SKILLS_NAME
SOFTWARE_SKILLS
SOFTWARE_SKILLS_NAME
ONET
ONET_NAME
ONET_2019
ONET_2019_NAME
CIP6
CIP6_NAME
CIP4
CIP4_NAME
CIP2
CIP2_NAME
SOC_2021_2
SOC_2021_2_NAME
SOC_2021_3
SOC_2021_3_NAME
SOC_2021_4
SOC_2021_4_NAME
SOC_2021_5
SOC_2021_5_NAME
LOT_CAREER_AREA
LOT_CAREER_AREA_NAME
LOT_OCCUPATION
LOT_OCCUPATION_NAME
LOT_SPECIALIZED_OCCUPATION
LOT_SPECIALIZED_OCCUPATION_NAME
LOT_OCCUPATION_GROUP
LOT_OCCUPATION_GROUP_NAME
LOT_V6_SPECIALIZED_OCCUPATION
LOT_V6_SPECIALIZED_OCCUPATION_NAME
LOT_V6_OCCUPATION
LOT_V6_OCCUPATION_NAME
LOT_V6_OCCUPATION_GROUP
LOT_V6_OCCUPATION_GROUP_NAME
LOT_V6_CAREER_AREA
LOT_V6_CAREER_AREA_NAME
SOC_2
SOC_2_NAME
SOC_3
SOC_3_NAME
SOC_4
SOC_4_NAME
SOC_5
SOC_5_NAME
LIGHTCAST_SECTORS
LIGHTCAST_SECTORS_NAME
NAICS_2022_2
NAICS_2022_2_NAME
NAICS_2022_3
NAICS_2022_3_NAME
NAICS_2022_4
NAICS_2022_4_NAME
NAICS_2022_5
NAICS_2022_5_NAME
NAICS_2022_6
NAICS_2022_6_NAME

Jobs_w_salary_info = Jobs.filter(
    ~(
        Jobs["SALARY"].is_null() |
        Jobs["SALARY_TO"].is_null() |
        Jobs["SALARY_FROM"].is_null()
    )
)

Jobs_w_salary_info.select(["SALARY_FROM", "SALARY_TO", "SALARY"]).describe()

Jobs_w_salary_info.shape

(30808, 131)

Jobs_w_salary_info.select(
    pl.col("EMPLOYMENT_TYPE_NAME").value_counts()
)

Jobs_w_salary_info.select(
    pl.col("EMPLOYMENT_TYPE").value_counts()
)

Jobs_w_salary_info.group_by("EMPLOYMENT_TYPE_NAME").agg([
    pl.len().alias("Job_Count"),
    pl.col("SALARY").mean().alias("Avg_Salary"),
    pl.col("SALARY").median().alias("Median_Salary"),
    pl.col("SALARY").min().alias("Min_Salary"),
    pl.col("SALARY").max().alias("Max_Salary"),
    pl.col("SALARY").std().alias("Salary_StdDev")
]).sort("Avg_Salary", descending=True)

df_plot = Jobs_w_salary_info.select(["EMPLOYMENT_TYPE_NAME", "SALARY_FROM"]).to_pandas()

employment_types = df_plot["EMPLOYMENT_TYPE_NAME"].unique()

plot_01 = go.Figure()

for emp_type in employment_types:
    plot_01.add_trace(
        go.Box(
            y=df_plot[df_plot["EMPLOYMENT_TYPE_NAME"] == emp_type]["SALARY_FROM"],
            name=emp_type,
            boxpoints='outliers',
            marker_color='lightgray',
            line_color='black',
            showlegend=False
        )
    )

plot_01.add_trace(
    go.Scatter(
        x=df_plot["EMPLOYMENT_TYPE_NAME"],
        y=df_plot["SALARY_FROM"],
        mode='markers',
        marker=dict(
            color=df_plot["SALARY_FROM"],
            colorscale='Viridis',
            showscale=True,
            size=6,
            opacity=0.7,
            colorbar=dict(title="Starting Salary")
        ),
        name='Salary Points',
        hoverinfo='x+y'
    )
)

plot_01.update_layout(
    title="Starting Salary Distribution by Employment Type",
    xaxis_title="Employment Type",
    yaxis_title="Starting Salary",
    xaxis_tickangle=-45,
    template='plotly_white',
    yaxis=dict(
        tick0=0,
        dtick=50000,
        gridcolor="lightgray"
    ),
    margin=dict(t=80, b=100, l=80, r=40),
    boxmode='group',
    boxgap=0.4,
    height=600,
    width=1000
)


plot_01.show()

df_plot = Jobs_w_salary_info.select(["NAICS2_NAME", "SALARY_FROM"]).to_pandas()

industries = df_plot["NAICS2_NAME"].unique()

plot_02 = go.Figure()

for industry in industries:
    plot_02.add_trace(
        go.Box(
            y=df_plot[df_plot["NAICS2_NAME"] == industry]["SALARY_FROM"],
            name=industry,
            boxpoints='outliers',
            marker_color='lightgray',
            line_color='black',
            showlegend=False
        )
    )

plot_02.add_trace(
    go.Scatter(
        x=df_plot["NAICS2_NAME"],
        y=df_plot["SALARY_FROM"],
        mode='markers',
        marker=dict(
            color=df_plot["SALARY_FROM"],
            colorscale='Turbo', 
            showscale=True,
            size=6,
            opacity=0.7,
            colorbar=dict(title="Starting Salary")
        ),
        name='Salary Points',
        hoverinfo='x+y'
    )
)

plot_02.update_layout(
    title="Starting Salary Distribution by Industry (NAICS2_NAME)",
    xaxis_title="Industry (NAICS2)",
    yaxis_title="Starting Salary",
    xaxis_tickangle=-45,
    template='plotly_white',
    yaxis=dict(
        tick0=0,
        dtick=50000,       
        gridcolor="lightgray"
    ),
    margin=dict(
        t=80,  
        b=120, 
        l=80,
        r=40
    ),
    boxmode='group',
    boxgap=0.4,  
    height=700,  
    width=1200   
)

plot_02.show()

Jobs_with_dates = Jobs.with_columns(
    pl.col("POSTED").cast(pl.Utf8).str.strptime(pl.Date, "%m/%d/%Y", strict=False).alias("POSTED_DATE")
)

daily_postings = (
    Jobs_with_dates
    .group_by("POSTED_DATE")
    .agg(pl.len().alias("Post_Count"))
    .sort("POSTED_DATE")
)

df_line = daily_postings.to_pandas()

df_line["Smoothed"] = df_line["Post_Count"].rolling(window=7, center=True).mean()

plot_03 = go.Figure()

# Raw daily line (gray)
plot_03.add_trace(go.Scatter(
    x=df_line["POSTED_DATE"],
    y=df_line["Post_Count"],
    mode="lines",
    name="Raw Daily Count",
    line=dict(color="lightgray", width=2)
))

# Smoothed line (red)
plot_03.add_trace(go.Scatter(
    x=df_line["POSTED_DATE"],
    y=df_line["Smoothed"],
    mode="lines",
    name="7-Day Smoothed",
    line=dict(color="red", width=3)
))

# Layout and formatting
plot_03.update_layout(
    title="Job Postings Over Time (Raw + 7-Day Smoothed)",
    xaxis_title="Date",
    yaxis_title="Number of Postings",
    template="plotly_white",
    height=500,
    width=1000,
    xaxis_tickangle=-45,
    margin=dict(t=60, b=100, l=60, r=40),
    legend=dict(x=0.01, y=0.99, bordercolor="gray", borderwidth=1)
)

plot_03.show()

Jobs_with_dates.select([
    pl.col("POSTED_DATE").min().alias("Earliest"),
    pl.col("POSTED_DATE").max().alias("Latest")
])

top_titles = (
    Jobs.group_by("TITLE_NAME")
        .agg(pl.len().alias("Count"))
        .sort("Count", descending=True)
        .limit(10)
)

df_top_titles = top_titles.to_pandas()

plot_04 = px.bar(
    df_top_titles,
    x="TITLE_NAME",
    y="Count",
    color="TITLE_NAME",  # Different color per title
    title="Top 10 Job Titles by Number of Postings",
    labels={"TITLE_NAME": "Job Title", "Count": "Number of Postings"},
    template="plotly_white"
)

plot_04.update_layout(
    xaxis_tickangle=-45,
    height=500,
    width=1000,
    showlegend=False,  # Optional: hide legend since x-axis already shows titles
    margin=dict(t=60, b=100, l=60, r=40)
)

plot_04.show()

Jobs.select("REMOTE_TYPE_NAME").unique()

Jobs_cleaned_remote = Jobs.with_columns(
    pl.when(
        pl.col("REMOTE_TYPE_NAME").is_null() | 
        (pl.col("REMOTE_TYPE_NAME").cast(pl.Utf8).str.strip_chars("[]") == "None")
    )
    .then(pl.lit("Unknown"))
    .otherwise(pl.col("REMOTE_TYPE_NAME"))
    .alias("REMOTE_TYPE_NAME")
)

Jobs_cleaned_remote.select("REMOTE_TYPE_NAME").unique()

remote_counts = (
    Jobs_cleaned_remote
    .group_by("REMOTE_TYPE_NAME")
    .agg(pl.len().alias("Count"))
    .sort("Count", descending=True)
)

df_remote = remote_counts.to_pandas()

plot_05 = px.pie(
    df_remote,
    names="REMOTE_TYPE_NAME",
    values="Count",
    title="Distribution of Remote Work Types",
    color_discrete_sequence=px.colors.qualitative.Set3
)

plot_05.update_traces(textposition='inside', textinfo='percent+label')

plot_05.update_layout(
    template="plotly_white",
    margin=dict(t=60, b=60, l=60, r=60)
)

plot_05.show()

Jobs.select("SKILLS_NAME").unique().shape

(44173, 1)

Jobs.select("SKILLS_NAME").unique().head(20)

exploded = Jobs.with_columns(
    pl.col("SKILLS_NAME").cast(pl.List(pl.Utf8))
).explode("SKILLS_NAME")

skills_per_industry = (
    exploded
    .group_by("NAICS_2022_6_NAME")
    .agg(pl.len().alias("Skill_Count"))
    .sort("Skill_Count", descending=True)
    .limit(15)
)

df_industry_skills = skills_per_industry.to_pandas()

plot_06 = px.bar(
    df_industry_skills,
    x="NAICS_2022_6_NAME",
    y="Skill_Count",
    title="Top 15 Industries by Skill Mentions",
    labels={
        "NAICS_2022_6_NAME": "Industry",
        "Skill_Count": "Total Skill Mentions"
    },
    template="plotly_white",
    color="NAICS_2022_6_NAME", 
    color_discrete_sequence=px.colors.qualitative.Pastel 
)

plot_06.update_layout(
    xaxis_tickangle=-45,
    height=700,
    width=1200,
    margin=dict(t=60, b=140, l=60, r=40),
    showlegend=False
)

plot_06.show()

Jobs.select("ONET_NAME").unique()

Jobs_w_salary_info.select("ONET_NAME").unique()

Jobs_w_salary_info.filter(
    pl.col("ONET_NAME") == "Business Intelligence Analysts"
).select(
    pl.median("SALARY").alias("Median_Salary")
)

plot_07 = px.bar(
    x=["Business Intelligence Analysts"],
    y=[Jobs_w_salary_info.filter(pl.col("ONET_NAME") == "Business Intelligence Analysts")
       .select(pl.median("SALARY"))[0, 0]],
    labels={"x": "[ONET] Occupation", "y": "Median Salary"},
    title="Median Salary: Business Intelligence Analysts",
    template="plotly_white"
)

plot_07.update_traces(marker_color='teal')

plot_07.show()

Jobs.select("SOC_2021_2_NAME").unique()

Jobs.group_by("SOC_2021_2_NAME").agg(
    pl.len().alias("Post_Count")
).sort("Post_Count", descending=True)

Jobs.select("SOC_2021_3_NAME").unique()

Jobs.group_by("SOC_2021_3_NAME").agg(
    pl.len().alias("Post_Count")
).sort("Post_Count", descending=True)

transitions = (
    Jobs.group_by(["SOC_2021_2_NAME", "SOC_2021_3_NAME"])
    .agg(pl.len().alias("Count"))
    .filter(pl.col("SOC_2021_2_NAME").is_not_null() & pl.col("SOC_2021_3_NAME").is_not_null())
)

df_transitions = transitions.to_pandas()

all_labels = pd.unique(
    np.concatenate([
        df_transitions["SOC_2021_2_NAME"].values,
        df_transitions["SOC_2021_3_NAME"].values
    ])
)

label_to_index = {label: idx for idx, label in enumerate(all_labels)}

df_transitions["source"] = df_transitions["SOC_2021_2_NAME"].map(label_to_index)
df_transitions["target"] = df_transitions["SOC_2021_3_NAME"].map(label_to_index)

plot_08 = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.5),
        label=list(label_to_index.keys()),
        color="lightblue"
    ),
    link=dict(
        source=df_transitions["source"],
        target=df_transitions["target"],
        value=df_transitions["Count"]
    )
)])

plot_08.update_layout(
    title_text="SOC Transitions: 2-Digit → 3-Digit Level",
    font_size=12,
    height=700,
    width=1000
)

plot_08.show()

ID	LAST_UPDATED_DATE	LAST_UPDATED_TIMESTAMP	DUPLICATES	POSTED	EXPIRED	DURATION	SOURCE_TYPES	SOURCES	URL	ACTIVE_URLS	ACTIVE_SOURCES_INFO	TITLE_RAW	BODY	MODELED_EXPIRED	MODELED_DURATION	COMPANY	COMPANY_NAME	COMPANY_RAW	COMPANY_IS_STAFFING	EDUCATION_LEVELS	EDUCATION_LEVELS_NAME	MIN_EDULEVELS	MIN_EDULEVELS_NAME	MAX_EDULEVELS	MAX_EDULEVELS_NAME	EMPLOYMENT_TYPE	EMPLOYMENT_TYPE_NAME	MIN_YEARS_EXPERIENCE	MAX_YEARS_EXPERIENCE	IS_INTERNSHIP	SALARY	REMOTE_TYPE	REMOTE_TYPE_NAME	ORIGINAL_PAY_PERIOD	SALARY_TO	SALARY_FROM	…	SOC_2021_5_NAME	LOT_CAREER_AREA	LOT_CAREER_AREA_NAME	LOT_OCCUPATION	LOT_OCCUPATION_NAME	LOT_SPECIALIZED_OCCUPATION	LOT_SPECIALIZED_OCCUPATION_NAME	LOT_OCCUPATION_GROUP	LOT_OCCUPATION_GROUP_NAME	LOT_V6_SPECIALIZED_OCCUPATION	LOT_V6_SPECIALIZED_OCCUPATION_NAME	LOT_V6_OCCUPATION	LOT_V6_OCCUPATION_NAME	LOT_V6_OCCUPATION_GROUP	LOT_V6_OCCUPATION_GROUP_NAME	LOT_V6_CAREER_AREA	LOT_V6_CAREER_AREA_NAME	SOC_2	SOC_2_NAME	SOC_3	SOC_3_NAME	SOC_4	SOC_4_NAME	SOC_5	SOC_5_NAME	LIGHTCAST_SECTORS	LIGHTCAST_SECTORS_NAME	NAICS_2022_2	NAICS_2022_2_NAME	NAICS_2022_3	NAICS_2022_3_NAME	NAICS_2022_4	NAICS_2022_4_NAME	NAICS_2022_5	NAICS_2022_5_NAME	NAICS_2022_6	NAICS_2022_6_NAME
str	str	str	i64	str	str	i64	str	str	str	str	str	str	str	str	i64	i64	str	str	bool	str	str	i64	str	i64	str	i64	str	i64	i64	bool	i64	i64	str	str	i64	i64	…	str	i64	str	i64	str	i64	str	i64	str	i64	str	i64	str	i64	str	i64	str	str	str	str	str	str	str	str	str	str	str	i64	str	i64	str	i64	str	i64	str	i64	str
"1f57d95acf4dc67ed2819eb12f049f…	"9/6/2024"	"2024-09-06 20:32:57.352 Z"	0	"6/2/2024"	"6/8/2024"	6	"[ "Company" ]"	"[ "brassring.com" ]"	"[ "https://sjobs.brassring.c…	"[]"	null	"Enterprise Analyst (II-III)"	"31-May-2024 Enterprise Analys…	"6/8/2024"	6	894731	"Murphy USA"	"Murphy USA"	false	"[ 2 ]"	"[ "Bachelor's degree" ]"	2	"Bachelor's degree"	null	null	1	"Full-time (> 32 hours)"	2	2	false	null	0	"[None]"	null	null	null	…	"Data Scientists"	23	"Information Technology and Com…	231010	"Business Intelligence Analyst"	23101011	"General ERP Analyst / Consulta…	2310	"Business Intelligence"	23101011	"General ERP Analyst / Consulta…	231010	"Business Intelligence Analyst"	2310	"Business Intelligence"	23	"Information Technology and Com…	"15-0000"	"Computer and Mathematical Occu…	"15-2000"	"Mathematical Science Occupatio…	"15-2050"	"Data Scientists"	"15-2051"	"Data Scientists"	"[ 7 ]"	"[ "Artificial Intelligence" …	44	"Retail Trade"	441	"Motor Vehicle and Parts Dealer…	4413	"Automotive Parts, Accessories,…	44133	"Automotive Parts and Accessori…	441330	"Automotive Parts and Accessori…
"0cb072af26757b6c4ea9464472a50a…	"8/2/2024"	"2024-08-02 17:08:58.838 Z"	0	"6/2/2024"	"8/1/2024"	null	"[ "Job Board" ]"	"[ "maine.gov" ]"	"[ "https://joblink.maine.gov…	"[]"	null	"Oracle Consultant - Reports (3…	"Oracle Consultant - Reports (3…	"8/1/2024"	null	133098	"Smx Corporation Limited"	"SMX"	true	"[ 99 ]"	"[ "No Education Listed" ]"	99	"No Education Listed"	null	null	1	"Full-time (> 32 hours)"	3	3	false	null	1	"Remote"	null	null	null	…	"Data Scientists"	23	"Information Technology and Com…	231010	"Business Intelligence Analyst"	23101012	"Oracle Consultant / Analyst"	2310	"Business Intelligence"	23101012	"Oracle Consultant / Analyst"	231010	"Business Intelligence Analyst"	2310	"Business Intelligence"	23	"Information Technology and Com…	"15-0000"	"Computer and Mathematical Occu…	"15-2000"	"Mathematical Science Occupatio…	"15-2050"	"Data Scientists"	"15-2051"	"Data Scientists"	null	null	56	"Administrative and Support and…	561	"Administrative and Support Ser…	5613	"Employment Services"	56132	"Temporary Help Services"	561320	"Temporary Help Services"
"85318b12b3331fa490d32ad014379d…	"9/6/2024"	"2024-09-06 20:32:57.352 Z"	1	"6/2/2024"	"7/7/2024"	35	"[ "Job Board" ]"	"[ "dejobs.org" ]"	"[ "https://dejobs.org/dallas…	"[]"	null	"Data Analyst"	"Taking care of people is at th…	"6/10/2024"	8	39063746	"Sedgwick"	"Sedgwick"	false	"[ 2 ]"	"[ "Bachelor's degree" ]"	2	"Bachelor's degree"	null	null	1	"Full-time (> 32 hours)"	5	null	false	null	0	"[None]"	null	null	null	…	"Data Scientists"	23	"Information Technology and Com…	231113	"Data / Data Mining Analyst"	23111310	"Data Analyst"	2311	"Data Analysis and Mathematics"	23111310	"Data Analyst"	231113	"Data / Data Mining Analyst"	2311	"Data Analysis and Mathematics"	23	"Information Technology and Com…	"15-0000"	"Computer and Mathematical Occu…	"15-2000"	"Mathematical Science Occupatio…	"15-2050"	"Data Scientists"	"15-2051"	"Data Scientists"	null	null	52	"Finance and Insurance"	524	"Insurance Carriers and Related…	5242	"Agencies, Brokerages, and Othe…	52429	"Other Insurance Related Activi…	524291	"Claims Adjusting"
"1b5c3941e54a1889ef4f8ae55b401a…	"9/6/2024"	"2024-09-06 20:32:57.352 Z"	1	"6/2/2024"	"7/20/2024"	48	"[ "Job Board" ]"	"[ "disabledperson.com", "d…	"[ "https://www.disabledperso…	"[]"	null	"Sr. Lead Data Mgmt. Analyst - …	"About this role: Wells Fargo …	"6/12/2024"	10	37615159	"Wells Fargo"	"Wells Fargo"	false	"[ 99 ]"	"[ "No Education Listed" ]"	99	"No Education Listed"	null	null	1	"Full-time (> 32 hours)"	3	null	false	null	0	"[None]"	null	null	null	…	"Data Scientists"	23	"Information Technology and Com…	231113	"Data / Data Mining Analyst"	23111310	"Data Analyst"	2311	"Data Analysis and Mathematics"	23111310	"Data Analyst"	231113	"Data / Data Mining Analyst"	2311	"Data Analysis and Mathematics"	23	"Information Technology and Com…	"15-0000"	"Computer and Mathematical Occu…	"15-2000"	"Mathematical Science Occupatio…	"15-2050"	"Data Scientists"	"15-2051"	"Data Scientists"	"[ 6 ]"	"[ "Data Privacy/Protection" …	52	"Finance and Insurance"	522	"Credit Intermediation and Rela…	5221	"Depository Credit Intermediati…	52211	"Commercial Banking"	522110	"Commercial Banking"
"cb5ca25f02bdf25c13edfede793150…	"6/19/2024"	"2024-06-19 07:00:00.000 Z"	0	"6/2/2024"	"6/17/2024"	15	"[ "FreeJobBoard" ]"	"[ "craigslist.org" ]"	"[ "https://modesto.craigslis…	"[]"	null	"Comisiones de $1000 - $3000 po…	"Comisiones de $1000 - $3000 po…	"6/17/2024"	15	0	"Unclassified"	"LH/GM"	false	"[ 99 ]"	"[ "No Education Listed" ]"	99	"No Education Listed"	null	null	3	"Part-time / full-time"	null	null	false	92500	0	"[None]"	"year"	150000	35000	…	"Data Scientists"	23	"Information Technology and Com…	231010	"Business Intelligence Analyst"	23101012	"Oracle Consultant / Analyst"	2310	"Business Intelligence"	23101012	"Oracle Consultant / Analyst"	231010	"Business Intelligence Analyst"	2310	"Business Intelligence"	23	"Information Technology and Com…	"15-0000"	"Computer and Mathematical Occu…	"15-2000"	"Mathematical Science Occupatio…	"15-2050"	"Data Scientists"	"15-2051"	"Data Scientists"	null	null	99	"Unclassified Industry"	999	"Unclassified Industry"	9999	"Unclassified Industry"	99999	"Unclassified Industry"	999999	"Unclassified Industry"

statistic	SALARY_FROM	SALARY_TO	SALARY
str	f64	f64	f64
"count"	30808.0	30808.0	30808.0
"null_count"	0.0	0.0	0.0
"mean"	96108.560277	139349.489938	117953.755031
"std"	38651.515935	57867.606046	45133.878359
"min"	10230.0	16640.0	15860.0
"25%"	66560.0	96471.0	84933.0
"50%"	90000.0	135000.0	116300.0
"75%"	119800.0	178200.0	145600.0
"max"	500000.0	500000.0	500000.0

EMPLOYMENT_TYPE_NAME	Job_Count	Avg_Salary	Median_Salary	Min_Salary	Max_Salary	Salary_StdDev
str	u32	f64	f64	i64	i64	f64
"Full-time (> 32 hours)"	29151	118897.558609	116500.0	20583	500000	44351.533443
"Part-time / full-time"	619	105621.242326	100000.0	20800	455375	52979.422642
"Part-time (â‰¤ 32 hours)"	1038	98802.509634	86390.0	15860	310050	55382.720356

EMPLOYMENT_TYPE_NAME
struct[2]
{"Part-time (â‰¤ 32 hours)",1038}
{"Full-time (> 32 hours)",29151}
{"Part-time / full-time",619}

EMPLOYMENT_TYPE
struct[2]
{1,29151}
{2,1038}
{3,619}

Earliest	Latest
date	date
2024-05-01	2024-09-30

REMOTE_TYPE_NAME
str
"Not Remote"
"Remote"
"Unknown"
"Hybrid Remote"